Many types of devices today include a digital camera that can be used to capture digital photos, such as with a mobile phone, tablet device, a digital camera, and other electronic media devices. The accessibility and ease of use of the many types of devices that include a digital camera makes it quite easy for most anyone to take photos. For example, rather than just having one camera to share between family members, such as on vacation, at a wedding, or for other types of family outings, each person may have a mobile phone and/or another device, such as a digital camera, that can be used to take photos of the vacation, wedding, and other types of events. Additionally, a user with a digital camera device is likely to take many more photos than in days past with film cameras, and the family may come back from a vacation, a family outing, or other event with hundreds, or even thousands, of digital photos. Further, a large number of the photos may be centered around the more important or interesting moments of an event. For example, during a wedding, most everyone will take photos of the ceremony, the cake cutting, the first dance, and other important moments. This can lead to an oversized collection of photos in a personal digital photo album, as well as many of the photos from the different people, that are duplicative photos of a few particular moments during an event.
With the proliferation of digital imaging, many thousands of images can be uploaded and made available for both private and public viewing. However, photo curation, which is a practice of sorting, organizing, and/or selecting, e.g., for sharing, can be very time-consuming with a large number of photos. For example, it may take hours to select the best or most important photos from a large number of photos. The importance of photos is typically selected from the viewpoint of the person sharing the photos, which can limit which photos are curated. Another disadvantage is that conventional photo curation techniques focus mainly on image quality (e.g., focus, exposure, composition, framing, and the like), aesthetics, visual similarity, and diversity measures for photo curation of a digital photo album.
Increasingly, convolutional neural networks are being developed and trained for computer vision tasks, such as for the basic tasks of image classification, object detection, and scene recognition. Generally, a convolutional neural network is self-learning neural network of multiple layers that are progressively trained, for example, to initially recognize edges, lines, and densities of abstract features, and progresses to identifying object parts formed by the abstract features from the edges, lines, and densities. As the self-learning and training progresses through the many neural layers, the convolutional neural network can begin to detect objects and scenes, such for object and image classification. Additionally, once the convolutional neural network is trained to detect and recognize particular objects and classifications of the particular objects, multiple images can be processed through the convolutional neural network for object identification and image classification.